Le Net participatif, levier d’acquisition des littératies traditionnelles et des littératies numériques | The Use of the Participative Web as a Lever for the Acquisition of Literacy and Digital Literacies
Bibliographic record
Abstract
Cet article présente les résultats d’une recherche de terrain, réalisée en mars 2014, au cours de laquelle des étudiants en 4e et 5e année d’alphabétisation des Cours Municipaux d’Adultes de la Ville de Paris ont publié, sous conduite pédagogique, des messages sur des forums de discussion et des commentaires d’articles d’un journal en ligne. L’objectif de nos recherches est de trouver les modalités d’une exploitation efficace des fonctionnalités du Net participatif afin que ce média puisse répondre aux besoins des apprenants, notamment en termes de littératie et de littératie numérique.In this paper, we will review the results of a field experiment carried out in March 2014 at the Cours Municipaux d’Adultes de la Ville de Paris (adult training provider of the City of Paris) with students in literacy years 4 and 5. The students were asked to post messages on discussion forums and in the commentary section of an online newspaper under pedagogical supervision. The aim of our study was to find the best strategies for the effective use of the participative web functionalities in order to answer the needs of learners, in terms of literacy and digital literacies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".